Can Increasing Input Dimensionality Improve Deep Reinforcement Learning?

3 March 2020

Papers citing "Can Increasing Input Dimensionality Improve Deep Reinforcement Learning?"

6 / 6 papers shown

Title
Hyperspherical Normalization for Scalable Deep Reinforcement Learning Hojoon Lee Youngdo Lee Takuma Seno Donghu Kim Peter Stone Jaegul Choo 70 1 0 24 Feb 2025
Cross-Domain Policy Adaptation by Capturing Representation Mismatch Jiafei Lyu Chenjia Bai Jingwen Yang Zongqing Lu Xiu Li 35 9 0 24 May 2024
Bridging State and History Representations: Understanding Self-Predictive RL Tianwei Ni Benjamin Eysenbach Erfan Seyedsalehi Michel Ma Clement Gehring Aditya Mahajan Pierre-Luc Bacon AI4TS AI4CE 29 21 0 17 Jan 2024
Intelligent DRL-Based Adaptive Region of Interest for Delay-sensitive Telemedicine Applications Abdulrahman Soliman Amr M. Mohamed Elias Yaacoub Nikhil V. Navkar A. Erbad 16 2 0 08 Oct 2023
A Survey on Transformers in Reinforcement Learning Wenzhe Li Hao Luo Zichuan Lin Chongjie Zhang Zongqing Lu Deheng Ye OffRL MU AI4CE 37 56 0 08 Jan 2023
Training Larger Networks for Deep Reinforcement Learning Keita Ota Devesh K. Jha Asako Kanezaki OffRL 37 39 0 16 Feb 2021